From Takeoff to Touchdown: Dissecting Data on Air Disasters

INFO 526 - Project Final

A shiny app integration with aircraft crash analysis
Author
Affiliation

Infographic Innovators - Antonio, Bharath, Eshaan, Thanoosha

School of Information, University of Arizona

Abstract:

We wanted to provide an in-depth analysis of aircraft crashes in the United States from 1980 to 2022, focusing on locations, timings, and consequences, along with exploring causes and weather conditions’ influence on these incidents.

  1. Question: What are the aircraft crashes’ locations, timings, and consequences?

    Objective: To discover if any correlations that contributed to aircraft crashes during a certain time period. As well as looking to see if specific locations have higher numbers of crashes than others.

    Methodology: Utilization of a detailed dataset from the National Transportation Safety Board, incorporating various data visualization techniques and time-series analyses. We created a heatmap on number of crashes in the different regions, and radial car plot to visualize crashes during certain flight phases.

    Findings: Overall, when looking at the crashes through 1980-2020, the number of total fatalities from crashes have decreased. There was a significant jump in 2001 which we concluded was from the 9/11 attacks. After this, the number of fatalities decreased significantly. The heatmap analysis revealed that Alaska, Arizona, Texas, and Florida had the highest number of crashes. The radial bar plot indicated specific flight phases where crashes were more prevalent.

  2. Question: What contributes to the crashes, and does weather significantly impact the increase in aircraft crashes?

    Objective: To identify and categorize the most frequent causes of crashes and to correlate crash causes with the severity of outcomes.

    Methodology: Using the dataset, we created a bar plot to show the common cause of aircraft crashes, a stacked area chart to show the causes and severity of crashes, and a radar plot to visualize crashes by month and weather conditions.

    Findings: Pilot error emerged as the leading cause of crashes, followed by mechanical failures like loss of engine power. The severity of crashes varied with different causes, as shown in the stacked area chart. Weather-related crashes showed distinct patterns in the radar plot, correlating specific weather conditions with increased crash occurrences in certain months.

    Analysis:

Question 1: Examining Aircraft Crashes, with a focus on their locations, timings, and consequences

Timeseries analysis of fatalities, and types of injuries.

Total Fatalities Over The Years

Animation of Total Fatalities

Timeseries Animations of Serious Injuries & Minor Injuries.

Animated Combined Timeseries of Aircraft Crashes

Findings

There has been a general decrease in the number of total fatalities from 1980 to 2022. A notable spike in fatalities was observed in 2001, attributed to the 9/11 attacks. Post-2001, a significant decline in fatalities was noted.

Heatmap– on number of crashes in different regions(US map)

Discussion: Looking at the heatmap Alaska, Arizona, Texas, and Florida has the highest number of crashes.

Questions 2: Analysis of Causes of Crashes

Waffle chart

Purpose: The waffle chart shows different causes of aircraft crashes. Pilot’s failure was the highest scoring cause of crashes, followed by loss of engine power.

Findings: - The waffle chart effectively showcased the distribution of crash causes, emphasizing the prominence of human error in aviation incidents. The data highlighted the need for enhanced safety measures and training to address the identified causes of crashes.

Density Plot

Purpose: We plotted the density plot using ggplot’s geom_density function is to visually analyze the distribution of flight crashes over the years based on their probable causes. By utilizing the probable_cause_flights dataset and focusing on the cause_summary column, this visualization aims to provide insights into the changing patterns and trends of aviation incidents. The x-axis represents the years, offering a chronological perspective, while the y-axis portrays the density of crashes associated with specific causes. Here, we have focused on the attribute Pilot's Failure

Discussion: This visual representation allows us to identify clusters of high density, indicating periods or years where certain causes were more prevalent. Additionally, it facilitates the detection of outliers or shifts in patterns, enabling a more nuanced exploration of the dataset. Here, we can see that injuries in particular have reduced overtime with the number of Fatal Injuries reducing significantly over the past few decades.

Assessing the Influence of Weather Conditions on Crashes

Radar Plot

Purpose: We plot the Radar Plot using Plotly for R to comprehensively assess the influence of weather conditions on flight crashes. Leveraging the flights_ntsb_radar dataset which we derived from the original flights_ntsb dataset and categorizing flight crashes based on Visual Meteorological Conditions (VMC) and Instrument Meteorological Conditions (IMC). This visualization seeks to highlight the varying degrees of impact these conditions have on aviation safety. By layering both datasets on a radar plot, we aim to provide a holistic perspective on how different weather scenarios contribute to flight incidents.

Discussion: The radar plot serves as an effective means to showcase the multivariate nature of weather conditions and their relationship with flight crashes. Each axis on the radar represents a specific parameter related to aviation safety, such as visibility, cloud cover, wind speed, and temperature. The radar plot allows for the simultaneous comparison of these parameters for VMC and IMC, unveiling patterns and discrepancies in their respective contributions to incidents.

Radial Bar Plot

Purpose: We plotted the radial bar plot to explore the distribution of flight crashes and associated injuries across different phases of flight. By categorizing flight phases into Landing, Takeoff, Approach, Maneuvering, Climb, and Other this visualization aims to uncover insights into the critical moments during a flight where incidents are more likely to occur. The first graph highlights the count of crashes in each phase, while the second graph focuses on the count of injuries, providing a broad perspective on the safety challenges associated with each phase.

Discussion: The radial bar plot offers an intuitive and visually appealing representation of the distribution of crashes and injuries throughout various phases of flight. In the first graph, the bars radiating from the center depict the count of crashes in each phase, allowing for a quick comparison of their frequencies. This visualization enables the identification of phases that might be particularly prone to incidents, guiding further investigation into the contributing factors.

The second graph, depicting injuries, provides an additional layer of analysis. By comparing the counts of injuries across different flight phases, we can discern whether certain phases are more likely to result in severe consequences. This insight is crucial for understanding the potential risks associated with specific segments of a flight, informing safety measures and protocols.

Conclusion

We can conclude stating that a lot of crashes take place every year but the number of crashes has been decreasing over the past few decades. The number of fatalities has also gone down due to the stringent rules in the Aviation industry. With the fatalities and crashes decreasing over time and more we are moving towards a safer and faster mode of transport which can get us around the globe in a span of a couple hours.

References

gganimate - https://r-graph-gallery.com/288-animated-barplot-transition.html ploty - https://plotly.com/r/animations/